NewBie image Exp0.1 is an efficient basic image generation model developed based on the Next-DiT architecture, specifically designed for generating high-quality anime-style images. It integrates advanced text encoders and visual components, supports natural language and structured tag input, and is a powerful tool for multi-character anime image generation.

Computer VisionDiffusersEnglish

FLUX.2-dev is an image generation and editing model based on NF4 quantized DiT and text encoder, providing high-quality image generation and editing capabilities, suitable for development applications in the image field.

Computer VisionDiffusersEnglish

This is a fine-tuned model based on the Cross Encoder architecture, specifically designed for text pair relevance scoring in semantic caching tasks. The model is fine-tuned on the LangCache Sentence Pairs dataset and can effectively calculate the similarity scores of text pairs, suitable for sentence pair classification and related tasks.

Natural Language ProcessingSentence-transformersEnglish

Sarashina2.2-Vision-3B is a large Japanese vision-language model trained by SB Intuitions. It is built based on Sarashina2.2-3B-Instruct and the SigLIP image encoder, and has powerful image-to-text conversion capabilities, supporting multimodal processing in Japanese and English.

MultimodalTransformersMultiple Languages

This is the third version of the monolingual encoder-decoder language model released by the HPLT project, specifically designed for German. It is a text-to-text transformer model trained based on the denoising objective, following the NorT5 architecture settings, and using a tokenizer trained with German HPLT data.

Natural Language ProcessingPytorchMultiple Languages

This is a Modal Balance Quantization (MBQ) variant of DeepSeek-OCR. It reduces memory usage by applying 4-bit NF4 quantization to the visual encoder while maintaining the BF16 precision of the projector and language/decoder. It can be locally deployed on ordinary GPUs and used for rapid CPU experiments.

MultimodalTransformers

This is a semantic similarity calculation model based on the Cross Encoder architecture. It fine-tunes the Reason-ModernColBERT base model on the LangCache sentence pair dataset. This model is specifically designed to calculate the similarity scores between text pairs and is suitable for tasks such as sentence pair classification and semantic caching.

Natural Language ProcessingSentence-transformersEnglish

This is a semantic reordering model based on Cross Encoder, specifically fine-tuned for the Redis LangCache semantic caching system. This model can effectively calculate the similarity score of text pairs and is suitable for sentence pair classification and semantic similarity calculation tasks.

Natural Language ProcessingSentence-transformersEnglish

MERaLiON-SER-v1 is a parameter-efficient multilingual speech emotion recognition model that can simultaneously predict 7 discrete emotion categories and continuous emotion dimension values. This model is built on the Whisper-Medium encoder and adopts the LoRA adaptation technology, showing excellent performance in the multilingual speech emotion recognition task.

Audio ProcessingTransformersMultiple Languages

Tesity-T5 is an encoder-decoder type language model developed by Clemylia, focusing on contextual Q&A tasks. This model can accurately extract and synthesize answers based on the provided text context, with the ability to provide factual and comprehensive responses.

Natural Language ProcessingTransformersFrench

This is a semantic re-ranking model based on the Cross Encoder architecture, specifically fine-tuned for the Redis LangCache semantic caching scenario. The model is trained on the LangCache Sentence Pairs dataset and can effectively calculate the semantic similarity scores of text pairs for sentence pair classification and re-ranking tasks.

Natural Language ProcessingSentence-transformersEnglish

Rapido NER is a powerful multilingual named entity recognition and entity embedding model that supports 55 languages. It integrates an encoder, attention-based mention pooling, a type projection layer, and conditional random field decoding, specifically designed to solve common problems in the field of named entity recognition.

Natural Language ProcessingTransformersMultiple Languages

ColModernVBERT is a late interaction version of ModernVBERT, specifically fine - tuned for the visual document retrieval task and is the best - performing model on this task. It is a compact vision - language encoder with 250 million parameters, achieving performance close to that of models with 10 times more parameters in visual document benchmark tests, while having a considerable inference speed on the CPU.

MultimodalSafetensorsEnglish

This is a Vision Transformer image feature encoder based on the DINOv3 architecture, obtained through knowledge distillation from the 7B-parameter DINOv3 ViT model on the LVD-1689M dataset. This model is specifically designed for image feature extraction tasks and has strong visual representation capabilities.

Computer VisionTimm

The ONNX compiled version decoder of NeuCodec, a lightweight audio decoding model designed for device-side text-to-speech (TTS), which can efficiently convert encoded audio features into high-quality audio signals.

Audio ProcessingOnnx

FastVLM is an efficient visual encoding scheme for vision-language models. It significantly reduces the encoding time of high-resolution images through the innovative FastViTHD hybrid visual encoder, improving model performance and response speed, and performs excellently in multiple vision-language understanding benchmark tests.

MultimodalSafetensors

FastVLM is an efficient vision-language model that significantly reduces the encoding time of high-resolution images and improves model performance through the innovative hybrid visual encoder FastViTHD. This model performs excellently in multiple visual question-answering and document understanding benchmark tests, while significantly reducing the time to generate the first word.

MultimodalSafetensors

FastVLM is an efficient visual coding scheme designed specifically for vision-language models. Through the innovative hybrid visual encoder FastViTHD, it significantly reduces the encoding time of high-resolution images and improves the model's response speed and performance. This model performs excellently in multiple visual question-answering and document understanding benchmark tests.

MultimodalSafetensors

all-MiniLM-L6-v2 is an efficient sentence embedding model in the sentence-transformers library, built on the MiniLM architecture. This model is specifically optimized for semantic similarity tasks at the sentence and paragraph levels, capable of significantly reducing computational resource requirements while maintaining high performance.

Natural Language ProcessingSentence-transformersEnglish

MiMo-VL is a compact and powerful vision-language model that combines a native resolution ViT encoder, an MLP projector, and the MiMo-7B language model. It performs excellently in tasks such as multimodal reasoning. This model shows outstanding performance in multiple benchmark tests, has a chain-of-thought control function, and significantly improves the user experience.

MultimodalTransformers